Problem
Loss prevention programs often depend on tribal knowledge: which stores are “bad,” which hours feel risky, and which cameras someone remembered to check.
Business impact
Shrink stays unexplained. Labor is spent on low-yield footage review. Regional leaders cannot see which interventions work.
Current approaches
- Store-by-store LP staffing
- Manual exception reports
- Periodic mystery shops
- Standalone analytics appliances
Why existing methods fail
- No standardized event language across stores
- Evidence is hard to retrieve under time pressure
- Ops and LP work from different systems
- ROI stories stay anecdotal
How AI solves it
- AI converts camera activity into reviewable loss-prevention events
- Evidence clips attach to each exception
- Trends roll up for regional leadership
- The same model covers shrink, safety, and operational exceptions
With AnomaAI, detections become business events with evidence—so teams review what matters and leaders see outcomes, not camera walls.
Business outcomes
- Measurable shrink investigation speed
- Shared LP playbooks
- Network-level visibility
- Clearer ROI narrative
Frequently Asked Questions
Is this only for big-box retail?
No. Grocery, convenience, pharmacy, and specialty formats can map cameras to their workflows.
How fast can we deploy?
Most sites start with existing cameras and zone mapping—days to weeks, not rip-and-replace projects.
Related use cases
Next step
Ready to see this on your cameras? Book a demo or explore Book a demo.
